A conversational assistant usually responds with information. An agentic system may plan a sequence of steps, use tools and change something in the world. That shift creates a different design problem. The user needs to understand not only what the system says, but what it intends to do, what it has already done and what remains under their control.
Make the plan visible
Before consequential work begins, summarize the intended actions, required inputs and important assumptions. For multi-step tasks, show progress in language that maps to the user’s goal rather than exposing unnecessary technical logs. Let users inspect or revise the plan when the scope changes.
Design permissions around impact
Not every action needs the same approval. Reading a document may be low risk; sending an email, changing a customer record or making a purchase may require explicit confirmation. Use least-privilege access and make the difference between drafting, preparing and executing unmistakable.
Communicate state and uncertainty
Show whether the agent is planning, waiting for input, executing, blocked or finished. If an external tool fails, explain what did and did not happen. Never imply that a task succeeded when the system only prepared a recommendation or received an ambiguous response.
Support interruption and undo
People should be able to pause a workflow, cancel pending actions and inspect results. Where rollback is possible, make it discoverable. Where it is not possible, explain the consequence before execution and preserve a useful record of what occurred.
Test long-tail scenarios
Test partial failures, duplicate actions, stale data, conflicting instructions, expired permissions and interruptions midway through a sequence. Include cases where the agent should stop and ask rather than guess. Reliability is an interaction quality, not only an infrastructure metric.
Design for accountable delegation
The promise of agentic AI is not that users surrender control. It is that they can delegate appropriate work with a clear understanding of boundaries and outcomes. The best experience makes automation feel capable while keeping responsibility, visibility and recovery within reach.